Parsing of Melody: Quantification and Testing of the Local Grouping Rules of Lerdahl and Jackendoff's A Generative Theory of Tonal Music
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In two experiments, the empirical parsing of melodies was compared with predictions derived from four grouping preference rules of A Generative Theory of Tonal Music (F. Lerdahl & R. Jackendoff, 1983). In Experiment 1 (n = 123), listeners representing a wide range of musical training heard two familiar nursery-rhyme melodies and one unfamiliar tonal melody, each presented three times. During each repetition, listeners indicated the location of boundaries between units by pressing a key. Experiment 2 (n = 33) repeated Experiment 1 with different stimuli: one familiar and one unfamiliar nursery-rhyme melody, and one unfamiliar, tonal melody from the classical repertoire. In all melodies of both experiments, there was good within-subject consistency of boundary placement across the three repetitions (mean r = .54). Consistencies between Repetitions 2 and 3 were even higher (mean r = .63). Hence, Repetitions 2 and 3 were collapsed. After collapsing, there was high between-subjects similarity in boundary placement for each melody (mean r = .62), implying that all participants parsed the melodies in essentially the same (though not identical) manner. A role for musical training in parsing appeared only for the unfamiliar, classical melody of Experiment 2. The empirical parsing profiles were compared with the quantified predictions of Grouping Preference Rules 2a (the Rest aspect of Slur/Rest), 2b (Attack-point), 3a (Register change), and 3d (Length change). Based on correlational analyses, only Attack-point (mean r = .80) and Rest (mean r = .54) were necessary to explain the parsings of participants. Little role was seen for Register change (mean r = .14) or Length change (mean r = ––.09). Solutions based on multiple regression further reduced the role for Register and Length change. Generally, results provided some support for aspects of A Generative Theory of Tonal Music, while implying that some alterations might be useful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it